When we start working on a Machine Learning/Data Science project, first we need to perform some data cleaning and data transformation to get a tidy dataset. Then, we need to perform some EDA(Exploratory Data Analysis) to find trends, patterns, and outliers in the given data. Once, we have a machine interpretable data in place, we choose an algorithm and train the model. Then, we evaluate it on the test data. Next, we can tune the hyper parameters of the model and retrain it to get a robust model. Once the model performance is acceptable, we deploy it to make predictions. Typically, we follow these steps in a Machine Learning model creation:

In this post “Building first Machine Learning model using Logistic Regression in Python“, we are going to create our first machine learning predictive model in a step by step way. We will be using scikit-learn library … More